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https://github.com/tushar2704/product-recommendation
This project endeavors to create a recommendation system that suggests products to users based on their historical interactions and preferences.
https://github.com/tushar2704/product-recommendation
artificial-intelligence data-science product python recommender-system tushar2704
Last synced: 19 days ago
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This project endeavors to create a recommendation system that suggests products to users based on their historical interactions and preferences.
- Host: GitHub
- URL: https://github.com/tushar2704/product-recommendation
- Owner: tushar2704
- License: apache-2.0
- Created: 2023-08-12T07:50:37.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-08-14T10:10:44.000Z (over 1 year ago)
- Last Synced: 2024-05-11T05:53:46.840Z (8 months ago)
- Topics: artificial-intelligence, data-science, product, python, recommender-system, tushar2704
- Language: Jupyter Notebook
- Homepage: https://tushar-aggarwal.com
- Size: 33.7 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# E-commerce Product Recommendation
## Problem Description
In today's rapidly evolving digital landscape, e-commerce platforms have become an integral part of our daily routines. With the surge in online shopping, e-commerce companies are relentlessly working to augment user experiences and bolster sales by offering tailor-made product recommendations. The "E-commerce Product Recommendation" project endeavors to create a recommendation system that suggests products to users based on their historical interactions and preferences.
## Background Information
E-commerce platforms gather vast volumes of data regarding user behavior, encompassing product views, purchases, ratings, and more. This reservoir of data holds invaluable insights that can be harnessed to provide personalized product recommendations to customers. The primary objective of the recommendation system is to predict products of interest to users, thereby enhancing user engagement, fostering customer contentment, and ultimately, elevating revenue for the e-commerce platform.
## Dataset Information
The project employs a dataset containing user interactions with a multitude of products on an e-commerce platform. This dataset comprises the subsequent columns:
1. **userId**: A distinctive identifier for each user.
2. **productId**: A unique identifier for each product.
3. **Rating**: The user's assigned rating for a product (if available).
4. **timestamp**: The timestamp of the interaction (not utilized in this project).
The dataset is structured as a CSV file and facilitates an examination of user preferences and behavior to construct an efficacious recommendation system.
## Problem Statement
The central challenge of this project lies in constructing an efficient recommendation system adept at handling copious amounts of data and supplying precise and pertinent product recommendations to users. The pivotal tasks encompassed in addressing this challenge are:
- **Data Preprocessing**: Addressing missing values, refining the dataset, and adapting it into a suitable format for recommendation models.
- **Exploratory Data Analysis**: Garnering insights into data distribution, scrutinizing user-product interaction trends, and identifying popular products and highly engaged users.
- **Recommendation Algorithms**: Implementing an array of recommendation algorithms, such as collaborative filtering, content-based filtering, and hybrid methodologies, to generate personalized product recommendations.
- **Model Evaluation**: Assessing the efficacy of distinct recommendation models using pertinent metrics to ensure that the system dispenses meaningful and valuable recommendations.
- **User Interface and Deployment**: Crafting an intuitive user interface to showcase product recommendations to users and seamlessly integrating the recommendation system into the e-commerce platform.
## Outcome
Successful execution of the "E-commerce Product Recommendation" project will culminate in a more immersive and individualized shopping journey for users. By proposing products aligned with each user's inclinations, the e-commerce platform can amplify user contentment, reinforce customer loyalty, and propel increased sales and revenue. Beyond this, the project underscores the potency of data-driven decision-making and underscores how recommendation systems can enrich user interactions while conferring advantages upon e-commerce enterprises.
## Project Structure
The project repository is organized as follows:
```
├── LICENSE
├── README.md <- README .
├── notebooks <- Folder containing the final reports/results of this project.
│ │
│ └── product.py <- Final notebook for the project.
├── reports <- Folder containing the final reports/results of this project.
│ │
│ └── Report.pdf <- Final analysis report in PDF.
│
├── src <- Source for this project.
│ │
│ └── data <- Datasets used and collected for this project.
| └── model <- Model.
```
## License
This project is licensed under the [MIT License](LICENSE).
## Author
- ©2023 Tushar Aggarwal. All rights reserved
- [LinkedIn](https://www.linkedin.com/in/tusharaggarwalinseec/)
- [Medium](https://medium.com/@tushar_aggarwal)
- [Tushar-Aggarwal.com](https://www.tushar-aggarwal.com/)
- [New Kaggle](https://www.kaggle.com/tagg27)
## Contact me!
If you have any questions, suggestions, or just want to say hello, you can reach out to us at [Tushar Aggarwal](mailto:[email protected]). We would love to hear from you!